code: def __init__(
      self,
      source: Union[str, Operation],
      decoder: Operation = None,
      transforms: List[Operation] = None,
  ):
      self.source = source
      self.decoder = decoder
      if transforms is None:
          transforms = []
      self.transforms = transforms
      self.default_pipeline = (
          decoder is None and not transforms and isinstance(source, str)
      )
  
file_path: ffcv/ffcv/pipeline/pipeline_spec.py
imports: import torch
  from typing import List, Union
  from .operation import Operation
  from ..transforms.module import ModuleWrapper
  from ..transforms import ToTensor
label_desc: Use image node_id 1 to create a new PipelineSpec object  with example: obj = PipelineSpec(source, decoder, transforms)